An embodiment of the invention is in general related to digital modulation communication systems and in particular to a receiver that can recover input data in the presence of multiplex/multi-access interference.
Communication systems are composed of at least one transmitter and at least one receiver. In digital modulation systems, input data (which is the information message to be communicated, such as a stream of binary bits) is encoded into nominal, digital symbols suitable for transmission over a particular channel. For example, with differential encoding, the information may be carried by the difference in phase between two successive waveforms. In some cases, a high frequency carrier waveform is modulated with the symbols (e.g. the symbols may be upconverted to passband), prior to being transmitted. At the receiver, the received communication signal may be demodulated (e.g. the symbols may be downconverted to baseband), prior to being decoded in an attempt to recover the input data.
In multiplex/multi-access communications, such as in a mobile telephone (e.g. cellular) network or a wireless computer network, a communication resource such as a carrier frequency is shared by many users who wish to transmit their input data simultaneously. The data for multiple units is transmitted over the same frequency band, but spread spectrum techniques are used to hold the mutual interference to manageable levels. The use of unique signature sequences for each transmission creates virtual channels within a single frequency band. A receiver may then be ‘tuned’ to a given channel, to detect the input data of a particular user. In this manner, each user may be assigned a separate virtual channel over which to communicate input data, to minimize interference with other users sharing the same communications resource. However, the ability of a receiver to detect this input data is impaired by the presence of noise (including interference) in its channel.
Various types of commonly occurring noise have been analyzed and mathematically modeled by those working in the field of communication systems. For example, there is the omnipresent additive white Gaussian noise. This noise may be represented by a term that is added to every transmitted signal. Also, certain channel environments have multiple paths from the transmitter to the receiver. These paths may be created by, for example, atmospheric reflections and refractions, and reflections from buildings or other objects. Such paths can cause a received communication signal to exhibit a multiplicative type of noise called multipath fading. Yet another type of noise, which is particularly apparent in a multiplex/multi-access communication system, is multiplex/multi-access interference caused by transmissions within the same frequency band but using different signature sequences (co-channel or intra-cell interference) and on adjacent frequency bands (inter-cell interference). This interference may be represented as an additive, time varying, noise term.
To compensate for noise which has corrupted a received communication signal, each receiver can be equipped with an adaptive filter. For example, in a typical differential encoding communication system, the adaptive filter can be positioned between a demodulation stage and a differential detection stage. The demodulation stage as mentioned above essentially removes the carrier waveform (if any) to yield a baseband communication signal. This signal contains the input data as differentially encoded into symbols, but corrupted by noise. The adaptive filter makes corrections to this signal according to a number of variable filter coefficients. The signal at the output of the adaptive filter is then processed by the differential detection stage which attempts to recover the input data using a differentially coherent detection scheme. An error signal, being a difference between the output of the detection stage and a reference, where this reference is predicted to be the input data that is sought by the detection stage, is generated. This error signal is fed back to the adaptive filter which in turn adjusts its coefficients according to an algorithm, in response to updates in the error signal. Over time, this closed loop feedback process is expected to converge to a set of filter coefficients that minimize the error signal and fully compensate for the noise. There is a trade-off between the speed of convergence and oscillation of the adaptive coefficients from their ideal values.